109 citations · 131 across the 5 of their papers we have counts for
6 papers · 1 filter
Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model
Ding Zhou, Yuanjun Gao, Liam Paninski
The Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) has been used widely as a natural Bayesian nonparametric extension of the classical Hidden Markov Model for learnin…
Neural Clustering Processes
Ari Pakman, Yueqi Wang, Catalin Mitelut +2
Probabilistic clustering models (or equivalently, mixture models) are basic building blocks in countless statistical models and involve latent random variables over discrete spaces…
Amortized Bayesian inference for clustering models
Ari Pakman, Liam Paninski
We develop methods for efficient amortized approximate Bayesian inference over posterior distributions of probabilistic clustering models, such as Dirichlet process mixture models.…
Nonlinear Evolution via Spatially-Dependent Linear Dynamics for Electrophysiology and Calcium Data
Daniel Hernandez, Antonio Khalil Moretti, Ziqiang Wei +3
Latent variable models have been widely applied for the analysis of time series resulting from experimental neuroscience techniques. In these datasets, observations are relatively…
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
Scott W. Linderman, Gonzalo E. Mena, Hal Cooper +2
Many matching, tracking, sorting, and ranking problems require probabilistic reasoning about possible permutations, a set that grows factorially with dimension. Combinatorial optim…
Partition Functions from Rao-Blackwellized Tempered Sampling
David Carlson, Patrick Stinson, Ari Pakman +1
Partition functions of probability distributions are important quantities for model evaluation and comparisons. We present a new method to compute partition functions of complex an…